A Holistic Approach to Model Discovery Using A Domain Ontology

Koo, Linsey; Trokanas, Nikolaos; Tokos, Hella; Cecelja, Franjo

doi:10.1016/b978-0-444-63428-3.50127-2

Cited by 2 publications

(4 citation statements)

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“…its functionality in terms of the process and/or unit it represents. In addition, each model is (semantically) described by requirements and other characteristics that form a comprehensive knowledge model (Koo et al (2016)) which includes model input(s), output(s), precondition(s), and the environment in which each process model operates (Trokanas et al 2014;Trokanas, Cecelja, et al 2015). The inputs and outputs are not limited to physical properties and can be extended to additional data or other properties.…”

Section: Model and Data Representationmentioning

confidence: 99%

“…The number of output variables can be purposely adjusted or extended to include additional data or parameters to consider the dynamic nature of models. Contrary to the models, data is semantically annotated with regards to output(s), functionality, and precondition(s) required to process data (Koo et al 2016). …”

Section: Model and Data Representationmentioning

confidence: 99%

“…The input/output matching facilitates interoperability between models and allows for the automated discovery of candidate models and datasets and hence support model integration (Raafat et al 2013;Koo et al 2016). The semantic relevance between the models is measured by the similarity measure , as defined by eq.…”

Section: Semantic Integration By Input/output Matchingmentioning

confidence: 99%

“…Following on previous developments and use of ontology to address challenges of identifying most suitable model or data to achieve the best solution for a particular engineering problem, ontology engineering is employed to describe them in a comprehensive manner to distinguish between them (Koo & Cecelja 2015;Koo et al 2016). It has been demonstrated that the differences of models and data can be addressed by explicit descriptions using defined terms to further improve consistency as well as understanding of the heterogeneity and concomitant consequences.…”

Section: Introductionmentioning

confidence: 99%

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A semantic framework for enabling model integration for biorefining

Koo

Trokanas

Cecelja

2017

Computers & Chemical Engineering

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Please cite this article as: Koo, Linsey., Trokanas, Nikolaos., & Cecelja, Franjo., A semantic framework for enabling model integration for biorefining. Computers and Chemical Engineering http://dx.doi.org/10.1016/j.compchemeng.2017.02.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A semantic framework for enabling model integration for biorefining Highlights We propose a semantic approach for establishing the interoperability between models and data.  Introduction of input/output matching as a mean of integration based on the structure of domain ontology.  The algorithm is implemented as a service.  Demonstration of performance is achieved using a real-life biorefining modelling scenario. AbstractThis paper introduces a new paradigm for establishing a framework that enables interoperability between process models and datasets using ontology engineering. Semantics are used to model the knowledge in the domain of biorefining including both tacit and explicit knowledge, which supports registration and instantiation of the models and datasets. Semantic algorithms allow the formation of model integration through input/output matching based on semantic relevance between the models and datasets. In addition, partial matching is employed to facilitate flexibility to broaden the horizon to find opportunities in identifying an appropriate model and/or dataset. The proposed algorithm is implemented as a web service and demonstrated using a case study.

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Section: Model and Data Representationmentioning

confidence: 99%

Section: Model and Data Representationmentioning

confidence: 99%

Section: Semantic Integration By Input/output Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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